{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "% matplotlib inline\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import sympy"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [],
   "source": [
    "x = np.linspace(-5, 2, 100)\n",
    "y1 = x ** 3 + 5 * x ** 2 + 10\n",
    "y2 = 3 * x ** 2 + 10 * x\n",
    "y3 = 6 * x + 10"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n"
     ]
    },
    {
     "data": {
      "text/plain": "<matplotlib.legend.Legend at 0x7f37ab702a90>"
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.plot(x, y1)\n",
    "ax.plot(x, y2)\n",
    "ax.plot(x, y3)\n",
    "ax.set_xlabel('x')\n",
    "ax.set_ylabel('y')\n",
    "ax.legend()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "outputs": [
    {
     "data": {
      "text/plain": "<matplotlib.collections.PathCollection at 0x7f37ab460820>"
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 3 Axes>",
      "image/png": "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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "figs, axes = plt.subplots(nrows=1, ncols=3)\n",
    "axes[0].step(x, y1)\n",
    "axes[1].bar(x, y2)\n",
    "axes[2].scatter(x, y3)\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "outputs": [
    {
     "data": {
      "text/plain": "[<matplotlib.lines.Line2D at 0x7f37ab5c0d00>]"
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}